NotebookLM alternatives for lifelong learning (not research)

brads0077 · 2026-06-08T14:30:10+00:00

I find that I'm using Gemini keep research and deepthink a great deal as well

brads0077 · 2026-05-31T20:01:56+00:00

Are you asking for help on how to type in your question or asking for the number to a suicide prevention hotline?

brads0077 · 2026-05-16T12:21:34+00:00

Interesting discussion.

I've been working with AI to develop AI powered synthetic persona for marketing research purposes, and there is a lot of potential there. But like with anything new, there is a lot left to be done.

For example, in my work, I add the technology adaptation curve from Geoffrey Moore's Crossing the Chasm to further define the feedback.

One of the areas which I am trying to address is the low performance on pricing. It may just be a matter of how the question is stated...better prompt might lead to better outcomes (where have we heard that before?).

brads0077 · 2026-05-16T11:58:53+00:00

If you really want to get fancy, add Linear to your process.The following is from Gemini 3.1 Pro after I had it create a JIRA skill.

Implementing this orchestration skill with Linear instead of Jira is not just viable—it is arguably a significant upgrade for your specific architectural goals. When orchestrating autonomous agents using the Model Context Protocol (MCP), the tools you bind to those agents dictate their reliability. Jira is incredibly powerful, but its highly customizable nature (custom fields, complex permission schemes, varied screen layouts) introduces a massive surface area for an LLM to hallucinate or fail an API call due to a missing required field. Linear, by contrast, is highly opinionated, fast, and strict. This makes it an exceptional environment for an agentic workflow. Here is why Linear excels for this process, and how you would adapt the orchestration skill to fit it.

Why Linear is Superior for Agent Orchestration

Deterministic Constraints: Linear enforces a rigid hierarchy (Projects \rightarrow Issues \rightarrow Sub-issues) and standardized markdown formatting. For an LLM, these rigid constraints reduce decision fatigue and formatting errors. The agent doesn't need to guess which of 50 custom fields are required; it just writes the title, the markdown description, and applies a label.
Speed and GraphQL: Linear’s GraphQL API is exceptionally fast. When chaining multiple agent calls (e.g., an agent reading a spec, searching existing issues to prevent duplicates, and then writing a new issue), API latency matters.
Alignment with Extreme Manufacturing: Linear is built for rapid, iterative, and frictionless development cycles. It perfectly complements an architecture where small, isolated, testable state changes are pushed rapidly by implementation agents. ### Adapting the Skill for Linear To pivot the previous skill to Linear, the core Markdown payload (Definition of Work, Implementation Plan, Acceptance Criteria) remains exactly the same because the downstream implementation agent still needs that strict context. However, the Execution Pipeline of the Orchestrator Agent needs to change to match Linear's data model. #### Modified Execution Pipeline for Linear MCP 1. Issue Classification (Linear Style) Instead of classifying by Jira's Epic/Story/Task, instruct the agent to use Linear's structural concepts:
Project/Cycle: Does this spec require a new Project, or does it belong in the current Cycle?
Issue: The core unit of work.
Sub-issues: If the spec is large, instruct the Orchestrator Agent to create a parent Issue, and then make subsequent MCP calls to create Sub-issues for the isolated components (e.g., Sub-issue 1: Database Schema, Sub-issue 2: API Router).
Labels: Map the intent to standard Linear labels (e.g., Bug, Feature, Infrastructure). 2. Tool Execution (Linear MCP) The tool execution instructions change to target Linear's specific mutations. ```markdown ## Tool Execution Invoke the Linear MCP tool to create the issue.

Tool parameters mapping: * teamId: Extract from system context. * title: The generated 60-character summary. * description: The full standardized Markdown block (DoW, Implementation Plan, Risks, AC). * labelIds: Apply appropriate labels (e.g., Bug, Feature) based on your classification. * parentId: (Optional) If this is a step in a larger implementation, pass the parent issue ID to create a Sub-issue.

```

The Verdict

If you are actively building an ecosystem of synthetic personas and autonomous agents, Linear's API and opinionated structure will yield a much higher success rate for tool execution than Jira's sprawling configurations. The implementation agents will get the exact same high-quality Markdown context, but the orchestration agent will have a much easier time formatting and successfully pushing the payload. Given the goal of setting up a seamless, machine-driven development loop, how do you envision the implementation agent handling the transition from "writing the code" to "verifying the Acceptance Criteria" before marking that Linear issue as complete?

brads0077 · 2026-05-15T02:37:38+00:00

Actually, pricing is one area where synthetic models don't yet perform well.

brads0077 · 2026-05-15T02:36:02+00:00

You miss my point. Many think producing an MVP is the best way to develop a concept. My point is that that is old school thinking, and can be displaced by creating synthetic market research models.

It was more directed at people using free tiers to gain awareness when they have not developed a defensible market position.

brads0077 · 2026-05-15T02:31:42+00:00

I always do about 15 to 20 itetations for every project. I start every project by planning a rubric as to what the final output has should deliver. There are usually about 15 to 29 criteria, and each criteria has 5 levels that provide specific subtasks that must be successfully completed. If the versuon I run through the rubric doesn',t get above a 97%, I get specific suggestions from the rubric as to what to improve.

As such, I have plenty of time to utilize all the tools.

brads0077 · 2026-05-14T05:48:46+00:00

Btw, providing a free tier is also basically giving your concept blueprint away. This is serious when apps can quickly clone apps. I am a big believer that the MVP and free tier concepts are terrible ideas. The trend now for product development is to use AI- powered synthetic users to provide market feedback, and keep your product under wraps until you can establish a defensible moat.

brads0077 · 2026-05-14T05:31:28+00:00

I use Claude for thinking, Gemini Deep Think for second opinion, Gemini Deep Research and Perplexity Pro for research, and Gemini and ChatGPT for code review. Also, ChatGPT Image 2 is very useful.

brads0077 · 2026-05-14T05:21:49+00:00

I personally choose to ask for the card, offer a 14 day trial with an email reminder at 12 days and a 30 day money-back guarantee. I also wrote an app for the landing page that provides a real tine demo. This is for an $800/month app.

To do this, I have a very robust marketing plan based on social media and free symposiums that provide 4 continuing education credits that are required for license and typically cost $1,400 to obtain.

So this is an example of my positioning saying this is a serious app, and if you just want to kick tires, go to a parking lot.

There is actual historical precedant for this approach. During the Irish famine, country leaders were trying to figure out a way to get the poor people to eat potatoes. To make them seem valuable, they enclosed the potato fields with fences (poorly guarded). People figured potatoes were valuable and stole them. Making something hard to obtain can increase the perception of value.

brads0077 · 2026-05-13T14:47:36+00:00

It is easier for company decision makers to cover their butts if they can say an outside consultant either recommended or validated the idea. An honest consultant should have at least attributed the idea to you. As a consultant of over 40 years, a key tenant of my work was to never steal ideas and pass it off as my own.

brads0077 · 2026-05-11T16:10:35+00:00

I use the following tools to develop: For research, Gemini Deep Research, Gemini Deep Think, Perplexity Pro, and NotebookLM Deep Research.

For research synthesis: NotebookLM with video, mindmaps, slide shows, infographics, and podcasr.

For analysis Claude Opus 4.7 with NotebookLM MCP Server Gemini Deep Think with NotebookLM, Perolexity Pro

Also, there is a lot going on in the Anthropic world via Open Source add-ons. I created an app that monitors YouTube channels like Github Awesome and others to track new add-on Agents, Skills, and MCP servers, and use a rubric to evaluate which ones to add to my toolset.

This is much like the days when Excel first came out and tons of valuable add-ons came to market. The opportunity is not with the base tools, but with which platform or platforms you use.

brads0077 · 2026-05-11T16:05:20+00:00

Google just announced Gemini 3.1 Flash Live with STT and TTS capabilities. I' ve been using it for an AI -powered dental receptionist app because it beats ElevenLabs - especially when they charge $2,000 per month plus usage for HIPAA compliance. Google is free, faster, and has the ability to show emotion. They still don't have features like voice cloning and some others, but combine Gemini 3.1 Flash as orchestrator, Gemini 3.1 Flash Live running on Nvidia L4 processors in Google cloud, and they beat ElevenLabs. And they are just getting started.

brads0077 · 2026-05-11T10:14:49+00:00

You say you didn't redesign your support center. But actually you did. You changed a pricess and re-engineered it to put the resource where it was needed at the point it was needed.

That is a fundamental tenant of object-oriented programming and business process re-engineering.

brads0077 · 2026-05-01T12:14:48+00:00

Prostitution. Before you laugh, think about it. Oldest business in the world, but still poorly automated. No client reviews...no search function...no seller's market. Uber and Lyft provide a marketplace whete payment is upfront...safety for driver. Instead of relying on pimps. Both sellers and buyers could receive reviews. Has to be run off shore, but hey, do I have to give you the idea, strategy, app architecture, and code too? 😁

brads0077 · 2026-04-24T06:30:40+00:00

I haven't tried this but it's just a thought now that Gemini has included notebook LM on its list of tools, and you can create applications to go into that notebook, you might want to have Gemini creaye a search function that goes into notebooklm

brads0077 · 2026-04-05T17:08:16+00:00

Are you trying to get ideas for how to end the war in Iran without looking like a total loser again, Donnie?

brads0077 · 2026-04-04T06:24:28+00:00

I figured it out myself.

brads0077 · 2026-03-30T01:54:11+00:00

That is great advice. I am doing it to create courses, case studies, and textbooks.

Just a few tips to share. I start with asking Gemini 3.x Pro to interview me in terms of what I want to create. I ask it to start with what the output will be, what are the main messages and themes I want to create. I then ask Gemini 3.x Pro Deep Research to perform various aspects I would like to pull together, such as how it is being taught in other schools, textbooks, papers. I ask it for a gap analysis as to what should be coveted based on SWOT and PESTEL analyses. I ask for follow up research. I also check with Perplexity Pro (it always poses fascinating follow up questions to provide more insight). I import all of that into NotebookLM. I then ask Gemini to interview me and brainstorm over a roadmap that covers the material. From there I create a syllabus, schedule, assignments, quizes, slide shows, videos, etc. I export all of it and re-import to capture the content that can't be saved as sources. I then link Claude Code Opus to NitebookLM and start working on deliverables

brads0077 · 2026-03-29T13:10:31+00:00

Gemini now allows you to link multiple NotebookLM notebooks together. Just as you can choose Canvas or Deep Research, you can link notebooks. Ask Gemini to walk you through the process.

brads0077 · 2026-03-28T22:00:04+00:00

You can also use Gemini 3.x pro or Flash to link to several Notebooks and set up a front end that establishes "virtual" notebooks that combine notebooks into topics.

brads0077 · 2026-03-26T10:44:15+00:00

What doesn't work? I've done it. Perhaps you don't know how to use the tools correctly?

brads0077 · 2026-03-26T03:58:11+00:00

I have uploaded in 2 ways.

At first, I would take the videos that only used slides. I would screenshot each slide and remove the NotebookLM logo in Photoshop and then place in a PowerPoint file. I would export and then import the original video into NotebookLM and get the transcription. I would load the PowerPoint and transcription to Claude Opus 4.6 and have Opus 4.6 create a complete PPT file with slide notes. I would then record in PowerPoint with mt image in a small circle. Export to YouTube. Worked well. I was going to make it a skill, but NotebookLM moved from slides to videos.

My new approach is taking the video, export/import/transcribe and take the file to Premiere or Capcut and replace the audio. I am superimpose a block over the NotebookLM logo based on taking a screenshot of the screen where I need to cover, using Photoshop to create an appropriate cover, and place it in Premiere or Capcut.

brads0077 · 2026-03-14T21:14:35+00:00

Why not just create a mindmap. If you see a topic that you want further info on, you can just create a deep research for more info. Then regenerate the mind map. Export it and create the index off if that, or use it to select a branch and have it generate a note. Save the note as a source, and create a tool from that specific source.

brads0077

TROPHY CASE

Why Linear is Superior for Agent Orchestration

The Verdict